Overview

Dataset statistics

Number of variables15
Number of observations4703
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory523.6 KiB
Average record size in memory114.0 B

Variable types

Numeric12
Boolean2
Categorical1

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
actor_1_fb_likes is highly overall correlated with other_actors_fb_likesHigh correlation
budget is highly overall correlated with gross and 1 other fieldsHigh correlation
country_UK is highly overall correlated with country_USAHigh correlation
country_USA is highly overall correlated with country_UKHigh correlation
critic_reviews_ratio is highly overall correlated with title_yearHigh correlation
gross is highly overall correlated with budget and 1 other fieldsHigh correlation
num_voted_users is highly overall correlated with budget and 1 other fieldsHigh correlation
other_actors_fb_likes is highly overall correlated with actor_1_fb_likesHigh correlation
title_year is highly overall correlated with critic_reviews_ratioHigh correlation
country_UK is highly imbalanced (56.6%)Imbalance
budget is highly skewed (γ1 = 49.02395721)Skewed
director_fb_likes has 825 (17.5%) zerosZeros
facenumber_in_poster has 2019 (42.9%) zerosZeros
movie_fb_likes has 2086 (44.4%) zerosZeros

Reproduction

Analysis started2024-04-11 22:27:01.952306
Analysis finished2024-04-11 22:27:29.366066
Duration27.41 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

duration
Real number (ℝ)

Distinct164
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.63066
Minimum14
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-12T00:27:29.520972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile84
Q194
median104
Q3118
95-th percentile146
Maximum330
Range316
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.562204
Coefficient of variation (CV)0.20769646
Kurtosis11.779179
Mean108.63066
Median Absolute Deviation (MAD)11
Skewness2.2280838
Sum510890
Variance509.05305
MonotonicityNot monotonic
2024-04-12T00:27:29.734691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 143
 
3.0%
100 134
 
2.8%
98 130
 
2.8%
101 130
 
2.8%
97 125
 
2.7%
93 120
 
2.6%
99 120
 
2.6%
94 120
 
2.6%
95 119
 
2.5%
106 108
 
2.3%
Other values (154) 3454
73.4%
ValueCountFrequency (%)
14 1
< 0.1%
20 1
< 0.1%
25 1
< 0.1%
34 1
< 0.1%
37 1
< 0.1%
41 1
< 0.1%
45 2
< 0.1%
46 1
< 0.1%
47 1
< 0.1%
53 1
< 0.1%
ValueCountFrequency (%)
330 1
< 0.1%
325 1
< 0.1%
300 1
< 0.1%
293 1
< 0.1%
289 1
< 0.1%
280 1
< 0.1%
271 1
< 0.1%
270 1
< 0.1%
251 2
< 0.1%
240 2
< 0.1%

director_fb_likes
Real number (ℝ)

ZEROS 

Distinct429
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean710.17223
Minimum0
Maximum23000
Zeros825
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-12T00:27:29.935294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median52
Q3209
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)201

Descriptive statistics

Standard deviation2861.8195
Coefficient of variation (CV)4.0297542
Kurtosis26.029513
Mean710.17223
Median Absolute Deviation (MAD)52
Skewness5.1181934
Sum3339940
Variance8190010.9
MonotonicityNot monotonic
2024-04-12T00:27:30.116972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 825
 
17.5%
3 65
 
1.4%
6 61
 
1.3%
7 58
 
1.2%
11 56
 
1.2%
2 56
 
1.2%
4 54
 
1.1%
10 51
 
1.1%
12 48
 
1.0%
5 48
 
1.0%
Other values (419) 3381
71.9%
ValueCountFrequency (%)
0 825
17.5%
2 56
 
1.2%
3 65
 
1.4%
4 54
 
1.1%
5 48
 
1.0%
6 61
 
1.3%
7 58
 
1.2%
8 47
 
1.0%
9 46
 
1.0%
10 51
 
1.1%
ValueCountFrequency (%)
23000 1
 
< 0.1%
22000 8
 
0.2%
21000 10
 
0.2%
18000 4
 
0.1%
17000 20
0.4%
16000 28
0.6%
15000 2
 
< 0.1%
14000 30
0.6%
13000 26
0.6%
12000 17
0.4%

actor_1_fb_likes
Real number (ℝ)

HIGH CORRELATION 

Distinct843
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6817.3957
Minimum0
Maximum640000
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-12T00:27:30.292812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile116.1
Q1637
median1000
Q311000
95-th percentile24000
Maximum640000
Range640000
Interquartile range (IQR)10363

Descriptive statistics

Standard deviation14982.445
Coefficient of variation (CV)2.1976786
Kurtosis720.98565
Mean6817.3957
Median Absolute Deviation (MAD)790
Skewness19.549467
Sum32062212
Variance2.2447365 × 108
MonotonicityNot monotonic
2024-04-12T00:27:30.531112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 417
 
8.9%
11000 207
 
4.4%
2000 187
 
4.0%
3000 148
 
3.1%
12000 133
 
2.8%
13000 126
 
2.7%
14000 121
 
2.6%
10000 109
 
2.3%
18000 108
 
2.3%
22000 79
 
1.7%
Other values (833) 3068
65.2%
ValueCountFrequency (%)
0 14
0.3%
2 6
0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 4
 
0.1%
6 3
 
0.1%
7 2
 
< 0.1%
9 2
 
< 0.1%
11 2
 
< 0.1%
12 3
 
0.1%
ValueCountFrequency (%)
640000 1
 
< 0.1%
260000 2
 
< 0.1%
164000 2
 
< 0.1%
137000 2
 
< 0.1%
87000 8
 
0.2%
77000 1
 
< 0.1%
49000 27
0.6%
46000 1
 
< 0.1%
45000 5
 
0.1%
44000 2
 
< 0.1%

gross
Real number (ℝ)

HIGH CORRELATION 

Distinct4146
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45085643
Minimum162
Maximum7.6050585 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-12T00:27:30.757224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile100669.6
Q16494675
median24848292
Q354548936
95-th percentile1.7099911 × 108
Maximum7.6050585 × 108
Range7.6050568 × 108
Interquartile range (IQR)48054262

Descriptive statistics

Standard deviation64148103
Coefficient of variation (CV)1.4228055
Kurtosis16.705866
Mean45085643
Median Absolute Deviation (MAD)20807704
Skewness3.3289438
Sum2.1203778 × 1011
Variance4.1149791 × 1015
MonotonicityNot monotonic
2024-04-12T00:27:31.001478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24848292 458
 
9.7%
5000000 4
 
0.1%
3000000 3
 
0.1%
218051260 3
 
0.1%
177343675 3
 
0.1%
8000000 3
 
0.1%
13401683 2
 
< 0.1%
800000 2
 
< 0.1%
22494487 2
 
< 0.1%
21028755 2
 
< 0.1%
Other values (4136) 4221
89.8%
ValueCountFrequency (%)
162 1
< 0.1%
423 1
< 0.1%
607 1
< 0.1%
703 1
< 0.1%
721 1
< 0.1%
728 1
< 0.1%
828 1
< 0.1%
1029 1
< 0.1%
1100 1
< 0.1%
1111 1
< 0.1%
ValueCountFrequency (%)
760505847 1
< 0.1%
658672302 1
< 0.1%
652177271 1
< 0.1%
623279547 1
< 0.1%
533316061 1
< 0.1%
474544677 1
< 0.1%
460935665 1
< 0.1%
458991599 1
< 0.1%
448130642 1
< 0.1%
436471036 1
< 0.1%

num_voted_users
Real number (ℝ)

HIGH CORRELATION 

Distinct4593
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87783.318
Minimum5
Maximum1689764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-12T00:27:31.201349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile1099.4
Q110774
median37952
Q3101938
95-th percentile343205.1
Maximum1689764
Range1689759
Interquartile range (IQR)91164

Descriptive statistics

Standard deviation140733.28
Coefficient of variation (CV)1.6031894
Kurtosis23.651174
Mean87783.318
Median Absolute Deviation (MAD)32809
Skewness3.9557772
Sum4.1284494 × 108
Variance1.9805856 × 1010
MonotonicityNot monotonic
2024-04-12T00:27:31.391593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3119 3
 
0.1%
2541 3
 
0.1%
3665 3
 
0.1%
9903 2
 
< 0.1%
6069 2
 
< 0.1%
80639 2
 
< 0.1%
25870 2
 
< 0.1%
1231 2
 
< 0.1%
3943 2
 
< 0.1%
53 2
 
< 0.1%
Other values (4583) 4680
99.5%
ValueCountFrequency (%)
5 2
< 0.1%
19 1
< 0.1%
28 1
< 0.1%
37 1
< 0.1%
40 1
< 0.1%
47 1
< 0.1%
48 1
< 0.1%
50 1
< 0.1%
53 2
< 0.1%
59 1
< 0.1%
ValueCountFrequency (%)
1689764 1
< 0.1%
1676169 1
< 0.1%
1468200 1
< 0.1%
1347461 1
< 0.1%
1324680 1
< 0.1%
1251222 1
< 0.1%
1238746 1
< 0.1%
1217752 1
< 0.1%
1215718 1
< 0.1%
1155770 1
< 0.1%

facenumber_in_poster
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3567935
Minimum0
Maximum43
Zeros2019
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-12T00:27:31.555384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum43
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0086637
Coefficient of variation (CV)1.4804491
Kurtosis55.770377
Mean1.3567935
Median Absolute Deviation (MAD)1
Skewness4.5646736
Sum6381
Variance4.03473
MonotonicityNot monotonic
2024-04-12T00:27:31.692055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2019
42.9%
1 1179
25.1%
2 665
 
14.1%
3 359
 
7.6%
4 190
 
4.0%
5 100
 
2.1%
6 67
 
1.4%
7 45
 
1.0%
8 34
 
0.7%
9 15
 
0.3%
Other values (9) 30
 
0.6%
ValueCountFrequency (%)
0 2019
42.9%
1 1179
25.1%
2 665
 
14.1%
3 359
 
7.6%
4 190
 
4.0%
5 100
 
2.1%
6 67
 
1.4%
7 45
 
1.0%
8 34
 
0.7%
9 15
 
0.3%
ValueCountFrequency (%)
43 1
 
< 0.1%
31 1
 
< 0.1%
19 1
 
< 0.1%
15 5
 
0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 4
 
0.1%
11 5
 
0.1%
10 10
0.2%
9 15
0.3%

budget
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct432
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39306827
Minimum218
Maximum1.22155 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-12T00:27:31.850027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile800000
Q17500000
median20000000
Q340000000
95-th percentile1.25 × 108
Maximum1.22155 × 1010
Range1.22155 × 1010
Interquartile range (IQR)32500000

Descriptive statistics

Standard deviation2.02669 × 108
Coefficient of variation (CV)5.1560762
Kurtosis2820.5211
Mean39306827
Median Absolute Deviation (MAD)15000000
Skewness49.023957
Sum1.8486001 × 1011
Variance4.1074723 × 1016
MonotonicityNot monotonic
2024-04-12T00:27:32.036861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000000 442
 
9.4%
30000000 145
 
3.1%
15000000 141
 
3.0%
25000000 139
 
3.0%
10000000 137
 
2.9%
40000000 131
 
2.8%
35000000 120
 
2.6%
50000000 104
 
2.2%
5000000 102
 
2.2%
60000000 94
 
2.0%
Other values (422) 3148
66.9%
ValueCountFrequency (%)
218 1
 
< 0.1%
1100 1
 
< 0.1%
4500 1
 
< 0.1%
7000 3
0.1%
9000 1
 
< 0.1%
10000 2
< 0.1%
14000 1
 
< 0.1%
15000 2
< 0.1%
20000 3
0.1%
22000 1
 
< 0.1%
ValueCountFrequency (%)
1.22155 × 10101
< 0.1%
4200000000 1
< 0.1%
2500000000 1
< 0.1%
2400000000 1
< 0.1%
2127519898 1
< 0.1%
1100000000 1
< 0.1%
1000000000 1
< 0.1%
700000000 2
< 0.1%
600000000 1
< 0.1%
553632000 1
< 0.1%

title_year
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.1112
Minimum1916
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-12T00:27:32.467446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1916
5-th percentile1978
Q11999
median2005
Q32010
95-th percentile2015
Maximum2016
Range100
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.50241
Coefficient of variation (CV)0.0062446132
Kurtosis7.3909201
Mean2002.1112
Median Absolute Deviation (MAD)6
Skewness-2.2877603
Sum9415929
Variance156.31026
MonotonicityNot monotonic
2024-04-12T00:27:32.649458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2009 252
 
5.4%
2006 235
 
5.0%
2008 222
 
4.7%
2010 221
 
4.7%
2011 215
 
4.6%
2005 215
 
4.6%
2014 214
 
4.6%
2013 213
 
4.5%
2004 206
 
4.4%
2012 203
 
4.3%
Other values (81) 2507
53.3%
ValueCountFrequency (%)
1916 1
< 0.1%
1920 1
< 0.1%
1925 1
< 0.1%
1927 1
< 0.1%
1929 2
< 0.1%
1930 1
< 0.1%
1932 1
< 0.1%
1933 2
< 0.1%
1934 1
< 0.1%
1935 1
< 0.1%
ValueCountFrequency (%)
2016 82
 
1.7%
2015 183
3.9%
2014 214
4.6%
2013 213
4.5%
2012 203
4.3%
2011 215
4.6%
2010 221
4.7%
2009 252
5.4%
2008 222
4.7%
2007 197
4.2%

aspect_ratio
Real number (ℝ)

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1255305
Minimum1.18
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-12T00:27:32.802051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile1.78
Q11.85
median2.35
Q32.35
95-th percentile2.35
Maximum16
Range14.82
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.63838629
Coefficient of variation (CV)0.3003421
Kurtosis377.18399
Mean2.1255305
Median Absolute Deviation (MAD)0
Skewness17.406589
Sum9996.37
Variance0.40753706
MonotonicityNot monotonic
2024-04-12T00:27:32.953951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2.35 2499
53.1%
1.85 1870
39.8%
1.37 97
 
2.1%
1.78 79
 
1.7%
1.66 63
 
1.3%
1.33 37
 
0.8%
2.2 14
 
0.3%
2.39 14
 
0.3%
16 8
 
0.2%
2 4
 
0.1%
Other values (10) 18
 
0.4%
ValueCountFrequency (%)
1.18 1
 
< 0.1%
1.2 1
 
< 0.1%
1.33 37
 
0.8%
1.37 97
2.1%
1.44 1
 
< 0.1%
1.5 2
 
< 0.1%
1.66 63
1.3%
1.75 3
 
0.1%
1.77 1
 
< 0.1%
1.78 79
1.7%
ValueCountFrequency (%)
16 8
 
0.2%
2.76 3
 
0.1%
2.55 2
 
< 0.1%
2.4 3
 
0.1%
2.39 14
 
0.3%
2.35 2499
53.1%
2.24 1
 
< 0.1%
2.2 14
 
0.3%
2 4
 
0.1%
1.85 1870
39.8%

movie_fb_likes
Real number (ℝ)

ZEROS 

Distinct836
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7779.7997
Minimum0
Maximum349000
Zeros2086
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-12T00:27:33.133029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median181
Q35000
95-th percentile41900
Maximum349000
Range349000
Interquartile range (IQR)5000

Descriptive statistics

Standard deviation19611.482
Coefficient of variation (CV)2.520821
Kurtosis40.309513
Mean7779.7997
Median Absolute Deviation (MAD)181
Skewness4.9742692
Sum36588398
Variance3.8461023 × 108
MonotonicityNot monotonic
2024-04-12T00:27:33.330902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2086
44.4%
1000 103
 
2.2%
11000 80
 
1.7%
10000 79
 
1.7%
12000 59
 
1.3%
13000 58
 
1.2%
2000 54
 
1.1%
15000 51
 
1.1%
14000 46
 
1.0%
16000 46
 
1.0%
Other values (826) 2041
43.4%
ValueCountFrequency (%)
0 2086
44.4%
4 2
 
< 0.1%
5 1
 
< 0.1%
7 2
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 2
 
< 0.1%
14 1
 
< 0.1%
16 1
 
< 0.1%
17 3
 
0.1%
ValueCountFrequency (%)
349000 1
< 0.1%
199000 1
< 0.1%
197000 1
< 0.1%
191000 1
< 0.1%
190000 1
< 0.1%
175000 1
< 0.1%
165000 1
< 0.1%
164000 1
< 0.1%
153000 1
< 0.1%
150000 1
< 0.1%

country_UK
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.3 KiB
False
4283 
True
 
420
ValueCountFrequency (%)
False 4283
91.1%
True 420
 
8.9%
2024-04-12T00:27:33.483566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

country_USA
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.3 KiB
True
3575 
False
1128 
ValueCountFrequency (%)
True 3575
76.0%
False 1128
 
24.0%
2024-04-12T00:27:33.591230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

other_actors_fb_likes
Real number (ℝ)

HIGH CORRELATION 

Distinct2041
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2380.9303
Minimum0
Maximum137748
Zeros32
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-12T00:27:33.728571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1480.5
median1017
Q31581
95-th percentile13000
Maximum137748
Range137748
Interquartile range (IQR)1100.5

Descriptive statistics

Standard deviation5262.0495
Coefficient of variation (CV)2.2100813
Kurtosis107.20966
Mean2380.9303
Median Absolute Deviation (MAD)547
Skewness6.8977295
Sum11197515
Variance27689165
MonotonicityNot monotonic
2024-04-12T00:27:33.908812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
0.7%
2000 30
 
0.6%
3000 19
 
0.4%
12000 16
 
0.3%
15000 16
 
0.3%
13000 15
 
0.3%
4000 15
 
0.3%
14000 14
 
0.3%
22000 13
 
0.3%
24000 12
 
0.3%
Other values (2031) 4521
96.1%
ValueCountFrequency (%)
0 32
0.7%
2 10
 
0.2%
3 3
 
0.1%
4 5
 
0.1%
5 7
 
0.1%
6 2
 
< 0.1%
7 4
 
0.1%
8 8
 
0.2%
9 6
 
0.1%
10 4
 
0.1%
ValueCountFrequency (%)
137748 1
 
< 0.1%
50000 1
 
< 0.1%
46000 1
 
< 0.1%
42000 1
 
< 0.1%
40000 2
 
< 0.1%
39000 1
 
< 0.1%
38000 1
 
< 0.1%
37000 2
 
< 0.1%
36000 8
0.2%
35000 1
 
< 0.1%

critic_reviews_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct3844
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8893224
Minimum0.037037037
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.5 KiB
2024-04-12T00:27:34.087193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.037037037
5-th percentile0.2016469
Q10.38334609
median0.62222222
Q31.0902912
95-th percentile2.2437546
Maximum25
Range24.962963
Interquartile range (IQR)0.7069451

Descriptive statistics

Standard deviation1.0070265
Coefficient of variation (CV)1.1323525
Kurtosis161.81569
Mean0.8893224
Median Absolute Deviation (MAD)0.29255189
Skewness9.1138071
Sum4182.4832
Variance1.0141023
MonotonicityNot monotonic
2024-04-12T00:27:34.259576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 43
 
0.9%
0.5 29
 
0.6%
0.3333333333 19
 
0.4%
2 19
 
0.4%
0.6666666667 16
 
0.3%
1.5 13
 
0.3%
0.8 12
 
0.3%
0.4 11
 
0.2%
0.5714285714 10
 
0.2%
3 8
 
0.2%
Other values (3834) 4523
96.2%
ValueCountFrequency (%)
0.03703703704 1
< 0.1%
0.04802123552 1
< 0.1%
0.05263157895 1
< 0.1%
0.05869565217 1
< 0.1%
0.0625 1
< 0.1%
0.06482504604 1
< 0.1%
0.07474352711 1
< 0.1%
0.07575757576 1
< 0.1%
0.07692307692 1
< 0.1%
0.07851239669 1
< 0.1%
ValueCountFrequency (%)
25 1
< 0.1%
21 2
< 0.1%
18 1
< 0.1%
9.428571429 1
< 0.1%
9 1
< 0.1%
8.75 1
< 0.1%
8.5 1
< 0.1%
8 2
< 0.1%
7 1
< 0.1%
6.759259259 1
< 0.1%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
2
3018 
1
1327 
3
 
204
0
 
154

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4703
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 3018
64.2%
1 1327
28.2%
3 204
 
4.3%
0 154
 
3.3%

Length

2024-04-12T00:27:34.410859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T00:27:34.526904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 3018
64.2%
1 1327
28.2%
3 204
 
4.3%
0 154
 
3.3%

Most occurring characters

ValueCountFrequency (%)
2 3018
64.2%
1 1327
28.2%
3 204
 
4.3%
0 154
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4703
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3018
64.2%
1 1327
28.2%
3 204
 
4.3%
0 154
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4703
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3018
64.2%
1 1327
28.2%
3 204
 
4.3%
0 154
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4703
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3018
64.2%
1 1327
28.2%
3 204
 
4.3%
0 154
 
3.3%

Interactions

2024-04-12T00:27:27.421536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:02.823968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:06.149590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:09.490526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:12.273187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:15.199871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:17.333050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:19.098472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:20.870381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:22.619054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:24.163421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:25.873916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:27.539815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:03.102293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:06.430518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:09.745509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:12.402237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:15.390603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:17.497682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:19.233458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:21.009405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:22.752251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:24.291554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:25.996108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:27.653054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:03.366146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:06.693276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:09.973143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:12.517653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:15.567451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:17.649847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:19.356615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:21.140738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:22.875614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:24.406891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:26.109752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:27.776412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:03.618250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:06.932458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:10.235747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:12.639442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:15.737054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:17.851598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:19.515364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:21.278025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:23.005155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:24.534870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:26.239260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:27.896978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:03.846046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:07.166881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:10.557804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:12.769540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:15.907641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:18.008916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:19.652738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:21.423238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:23.165829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:24.665470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:26.369029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:28.029863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:04.102558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:07.489084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:10.953553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:12.926347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:16.074269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:18.164125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:19.795177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:21.573517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:23.304930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:24.797427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:26.501658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:28.171235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:04.343373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:07.773511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:11.194647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:13.058914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:16.247051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:18.298271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:20.065105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:21.691552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:23.423818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:24.917915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:26.618521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:28.293403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:04.653200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:08.064979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:11.403904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:13.203044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:16.430173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:18.437063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:20.187713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:21.814858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:23.551345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:25.047210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:26.746493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:28.412271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:04.969691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:08.408737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:11.569006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:14.416151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:16.598053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:18.563182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:20.306224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:21.953408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:23.669961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:25.170363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:26.868116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:28.537602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:05.277139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:08.669327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:11.760889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:14.619731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:16.782492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:18.696202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:20.440699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:22.136400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:23.792245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:25.302373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:27.000434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:28.662328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:05.582808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:08.965903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:11.952484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:14.852840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:16.962065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:18.832747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:20.591346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:22.313571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:23.921803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:25.429369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:27.133645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:28.789745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:05.873553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:09.249487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:12.126523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:15.037341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:17.144993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:18.977537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:20.742237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:22.482402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:24.048155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:25.754921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T00:27:27.293734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-04-12T00:27:34.628123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
actor_1_fb_likesaspect_ratiobudgetcountry_UKcountry_USAcritic_reviews_ratiodirector_fb_likesdurationfacenumber_in_postergrossimdb_classificationmovie_fb_likesnum_voted_usersother_actors_fb_likestitle_year
actor_1_fb_likes1.0000.1450.3890.0000.015-0.1040.1430.2120.1160.3170.0290.1120.4320.7370.119
aspect_ratio0.1451.0000.2660.0000.0000.0820.0570.2160.0350.0940.0000.0760.1230.1150.290
budget0.3890.2661.0000.0000.050-0.1380.1730.3360.0260.5790.0000.0970.5010.3840.143
country_UK0.0000.0000.0001.0000.5560.025-0.0120.0540.003-0.0940.1080.0000.007-0.067-0.017
country_USA0.0150.0000.0500.5561.000-0.1250.050-0.0480.0330.2690.1060.0240.1160.266-0.051
critic_reviews_ratio-0.1040.082-0.1380.025-0.1251.000-0.064-0.2340.055-0.2930.0040.106-0.325-0.1250.580
director_fb_likes0.1430.0570.173-0.0120.050-0.0641.0000.1990.0080.1610.1390.0430.2560.117-0.019
duration0.2120.2160.3360.054-0.048-0.2340.1991.0000.0490.2440.2090.1070.3580.169-0.075
facenumber_in_poster0.1160.0350.0260.0030.0330.0550.0080.0491.000-0.0220.007-0.012-0.0410.1130.064
gross0.3170.0940.579-0.0940.269-0.2930.1610.244-0.0221.0000.1220.1040.6270.3550.022
imdb_classification0.0290.0000.0000.1080.1060.0040.1390.2090.0070.1221.0000.1080.373-0.008-0.127
movie_fb_likes0.1120.0760.0970.0000.0240.1060.0430.107-0.0120.1040.1081.0000.2150.0980.273
num_voted_users0.4320.1230.5010.0070.116-0.3250.2560.358-0.0410.6270.3730.2151.0000.3780.029
other_actors_fb_likes0.7370.1150.384-0.0670.266-0.1250.1170.1690.1130.355-0.0080.0980.3781.0000.105
title_year0.1190.2900.143-0.017-0.0510.580-0.019-0.0750.0640.022-0.1270.2730.0290.1051.000

Missing values

2024-04-12T00:27:28.962999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-12T00:27:29.238955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

durationdirector_fb_likesactor_1_fb_likesgrossnum_voted_usersfacenumber_in_posterbudgettitle_yearaspect_ratiomovie_fb_likescountry_UKcountry_USAother_actors_fb_likescritic_reviews_ratioimdb_classification
0178.00.01000.0760505847.08862040.0237000000.02009.01.7833000FalseTrue1791.00.2367392
1169.0563.040000.0309404152.04712200.0300000000.02007.02.350FalseTrue6000.00.2439422
2148.00.011000.0200074175.02758681.0245000000.02015.02.3585000TrueFalse554.00.6056342
3164.022000.027000.0448130642.011443370.0250000000.02012.02.35164000FalseTrue46000.00.3010003
5132.0475.0640.073058679.02122041.0263700000.02012.02.3524000FalseTrue1162.00.6260162
6156.00.024000.0336530303.03830560.0258000000.02007.02.350FalseTrue15000.00.2060992
7100.015.0799.0200807262.02948101.0260000000.02010.01.8529000FalseTrue837.00.8372092
8141.00.026000.0458991599.04626694.0250000000.02015.02.35118000FalseTrue40000.00.5684872
9153.0282.025000.0301956980.03217953.0250000000.02009.02.3510000TrueFalse21000.00.3854062
10183.00.015000.0330249062.03716390.0250000000.02016.02.35197000FalseTrue6000.00.2229952
durationdirector_fb_likesactor_1_fb_likesgrossnum_voted_usersfacenumber_in_posterbudgettitle_yearaspect_ratiomovie_fb_likescountry_UKcountry_USAother_actors_fb_likescritic_reviews_ratioimdb_classification
5026110.0107.0576.0136007.039241.04500.02004.02.35171FalseFalse178.02.0769232
502790.0397.05.0673780.045550.010000.02000.01.85697FalseFalse0.02.4615382
5029111.062.089.094596.063180.01000000.01997.01.85817FalseFalse19.01.5600002
503298.03.0789.024848292.04381.020000000.01995.02.3520FalseTrue346.00.7142862
503377.0291.0291.0424760.0726390.07000.02004.01.8519000FalseTrue53.00.3854452
503480.00.00.070071.05890.07000.02005.02.3574FalseFalse0.01.0000002
503581.00.0121.02040920.0520550.07000.01992.01.370FalseTrue26.00.4307692
503795.00.0296.04584.013381.09000.02011.02.35413FalseTrue338.01.0000002
503887.02.0637.024848292.06292.020000000.02013.02.3584FalseFalse788.00.1666672
504290.016.086.085222.042850.01100.02004.01.85456FalseTrue39.00.5119052

Duplicate rows

Most frequently occurring

durationdirector_fb_likesactor_1_fb_likesgrossnum_voted_usersfacenumber_in_posterbudgettitle_yearaspect_ratiomovie_fb_likescountry_UKcountry_USAother_actors_fb_likescritic_reviews_ratioimdb_classification# duplicates
0101.03.0448.012189514.0300920.023000000.02004.02.350FalseTrue263.00.50387622